News
  New! Feb 2022:I am joining Huawei Noah's Ark Lab in Montreal as an Intern.
  New! Nov 2021: One paper accepted as an oral to NeurIPS 2021 workshop on self-supervised learning.
  Feb 2021:One paper accepted to IEEE TMM.
  January 2021: I am joining National Research Council of Canada as an Intern
  September 2020: I am serving as a reviewer in AAAI 2021
  August 2020: I am presenting 1 paper in ECCV 2020
  August 2020: I am joining University of Waterloo and Vector Instituite as a Ph.D. student
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Research
I'm interested in machine learning and its application on computer vision. I am particularly interested in self-supervised learning and adversarial machine learning.
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f-mutual Information Contrastive Learning
Guojun Zhang*,
Yiwei Lu*,
Sun Sun,
Hongyu Guo,
Yaoliang Yu
NeurIPS 2021 workshop on self-supervised learning   (Contributed Talk)
paper/
poster/
talk
We propose a general and novel loss function on contrastive learning based on f-mutual information.
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AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting
Mahesh Kumar Krishna Reddy,
Mrigank Rochan,
Yiwei Lu,
Yang Wang
IEEE Transactions on Multimedia (TMM), 2021  
arXiv /
code
We propose a new problem called unlabeled scene adaptive crowd counting.
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Few-shot Scene-adaptive Anomaly Detection
Yiwei Lu,
Frank Yu,
Mahesh Kumar Krishna Reddy,
Yang Wang
ECCV, 2020   (Spotlight)
arXiv /
code
We propose a more realistic problem setting for anomaly detection in surveillance videos and solve it using a meta-learning based algorithm.
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Structure Learning with Similarity Preserving
Zhao Kang,
Xiao Lu,
Yiwei Lu,
Chong Peng,
Wenyu Chen,
Zenglin Xu
Neural Networks, 2020
arXiv
We propose a structure learning framework that retains the pairwise similarities between the data points.
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Future Frame Prediction Using Convolutional VRNN for Anomaly Detection
Yiwei Lu,
Mahesh Kumar Krishna Reddy,
Seyed shahabeddin Nabavi,
Yang Wang
AVSS, 2019
arXiv /
code
We propose a novel sequential generative model based on variational autoencoder (VAE) for future frame prediction with convolutional LSTM (ConvLSTM).
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Similarity Learning via Kernel Preserving Embedding
Zhao Kang,
Yiwei Lu,
Yuanzhang Su,
Changsheng Li,
Zenglin Xu
AAAI, 2019
PDF
We propose a novel similarity learning framework by minimizing the reconstruction error of kernel matrices, rather than the reconstruction error of original data adopted by existing work.
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Semantic Segmentation in Compressed Videos
Yiwei Lu,
Ang Li,
Yang Wang
MMSP, 2019
PDF
We propose a ConvLSTM-based model to perform semantic segmentation on compressed videos directly. This significantly speed up the training and test speed.
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